ABSTRACT Nonmedical opioid (NMO) use (misuse of either prescription opioids or heroin) and related overdose and mortality are rapidly growing public health problems. In particular, there is quite a bit of evidence that the NMO epidemic is growing particularly rapidly outside of major city centers and urban areas (i.e., in suburban and rural areas). While there has been a great deal of empirical evidence suggesting that features of physical and social environments, taken together (i.e., using a built environment framework) represent strong predictors of drug use and mental health outcomes in urban settings, there is a dearth of research assessing the built environmental features of non-urban settings in order to predict risk for NMO outcomes. The proposed study will compile data from secondary data sources for 566 municipalities in New Jersey to address this gap. New Jersey was chosen for its epidemiological relevance and its availability of NMO overdose data. In recent years, the highest rates of NMO overdose emergency room admissions have occurred in counties comprised of suburban and rural areas. The proposed study will be the first to systematically measure physical and social environmental features, i.e., the built environments, of non-urban areas which are theoretically and empirically related to NMO use, in the service of developing a built environment framework that can estimate municipality-level risk of NMO use and overdose in non-urban settings. This study will address the following specific aims: Aim 1. 1a. To develop a measurement strategy that extends use of the built environment framework to describe features of the physical and social environments of non-urban areas which are theoretically relevant for NMO use and overdose. 1b.To construct a spatial data infrastructure of built environment data to be utilized in a Geographic Information System (GIS) with which to test the feasibility and validity of this new built environment measure among both urban and non-urban communities. Aim 2. To assess the validity of the measure produced in Aim 1 by examining its relationship to various social and environmental constructs. We will assess the predictive validity of the new measure in part by examining its ability to predict areas at higher risk for overdose at the municipality level (among both urban and non-urban areas). We will also assess correlations of the new measure with other municipality level variables in order to establish concurrent, convergent, and discriminant validity. The development of this new built environment measure and corresponding spatial data infrastructure can be replicated, thereby allowing public health departments and other service organizations to identify specific areas with greatest risk for NMO morbidity and mortality. This, in turn, will allow them to strategically allocate resources to these areas and to design and/or modify their prevention and intervention efforts to address area vulnerabilities and to more directly and efficiently target high-risk populations.